Microstrategy, Inc.

Scott Cappiello

Benjamin Z Li

Jun Yuan

Xun Feng

William Hurwood

Rixin Liao

Yuliyan Kiryakov

Gang Liang

Jeffrey A Bedell

Ramkumar Ramachandran

Luis V Orozco

Yi Du

Wenfeng Li

Stephen Scott Trundle

Michael Jerry Saylor

Abhimanyu Warikoo

L Douglas Everhart

Fabrice Martin

Steven S Trundle

Yi Luo

Ramprasad Polana

David B Sherwood

Ningning Liu

Alejandro Freixas

Raul Gonzalez

Gaurav Rewari

Chris Richardson

Dyung V Le

Patrick Vinton

Rodolfo Avalos

Search integration is described. The actions include receiving, by one or more processors, a query. The actions include identifying search results that are responsive to the query. The actions include identifying, based on the query and the search results, structured data to supplement the search results, wherein the structured data is generated from binary table data that is received and deserialized by one or more processing modules. The actions include processing the structured data and the search results. The actions include providing, for output, the processed search results and the processed structured data results.

Search integration

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimization of memory analytics are disclosed. In one aspect, a method includes receiving, by a data storage system, a request to generate a visualization of a portion of data stored in the data storage system. The method includes providing, for display, the visualization of the portion of the data. The method includes accessing historical data that includes data indicating actions performed on previously displayed visualizations and corresponding portions of the data. The method includes generating an action to perform on the visualization and the portion of the data. The method further includes performing, in background, the generated action on the visualization and the portion of the data and suppressing results of the generated action. The method includes receiving a user specified action to perform on the visualization and the portion of the data.

Optimization of memory analytics

Data engine integration and data refinement are described. The actions include receiving, by a data refinement engine, a request for data. The actions include determining a first amount of processing to be performed by the data refinement engine and a second amount of processing to be performed by one or more processors of a data source that include a plurality of data nodes. The actions include transmitting, by the data refinement engine, code to the plurality of data nodes of instructions associated with the second amount of processing. The actions include receiving, by the data refinement engine and from the plurality of data nodes, unprocessed first data and processed second data. The actions include processing, by the data refinement engine, the unprocessed first data. The actions include, in response to the request for data, transmitting, by the data refinement engine, the processed first data and the processed second data.

Data engine integration and data refinement

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for memory analytics are disclosed. In one aspect, a method includes receiving input that requests a particular number of data items that are highest ranking in the data storage system based on a characteristic. The method includes accessing sets of data items to use in satisfying the request by selecting, from each of the independent data partitions, a set of data items that includes less than the particular number of data items that are highest ranking in the respective independent data partition based on the characteristic. The method includes comparing the sets of data items accessed from the plurality of independent data partitions. The method includes selecting the particular number of data items that are ranked highest based on the characteristic. The method includes providing, for output, the selected particular number of data items.

Memory analytics

Data engine integration and data refinement are described. The actions include receiving, by an in-memory engine, a request for data. The actions include transmitting, by the in-memory engine and to a metadata storage unit, data that is associated with the request. The actions include receiving metadata. The actions include determining a first amount of processing to be performed by the in-memory engine and a second amount of processing to be performed by a data engine. The actions include transmitting a request (i) for a first portion of unprocessed data and (ii) to perform a second amount of processing on a second portion of unprocessed data. The actions include receiving the first portion of unprocessed data and the second portion of processed data. The actions include processing the first portion of unprocessed data. The actions include, providing the first portion of processed data and the second portion of processed data.

Systems and methods are disclosed for distributing an in-memory data store over a plurality of independent data partitions. For example, the method includes associating each of the plurality of independent data partitions with at least one of a plurality of processing units such that one or more data sets in a corresponding each of the plurality of independent data partitions are processed by the at least one of the plurality of processing units. A query execution engine is provided for causing the plurality of processing units to execute, in parallel, a series of queries to the plurality of independent data partitions.

Parallel query processing

Database report generation technology for handling a single logical query that requires data that is physically located in two different sources. The system uses intelligent push-down joins, which move data from one source to the other and join data within that source. The system dynamically determines which direction to move data depending on how the data is used in a query.

Data source joins

Dynamic sourcing, in which a data request that is associated with a query is received and a parameter of data needed for satisfaction of the query is identified. Parameter information defining data available in at least one cube stored in a cache is accessed and the parameter is compared with the parameter information. Based on comparison results, it is determined whether one or more cubes in the cache include sufficient data to satisfy the query. In response to a determination that one or more cubes include sufficient data to satisfy the query, a response to the data request is generated by executing the query against the one or more cubes. In response to a determination that the cubes do not include sufficient data to satisfy the query, a response to the data request is generated by executing at least a portion of the query against a database system.

Dynamic sourcing

A technique for exporting results from a reporting system report in a format type including a datamart, PDF file, flat file, Excel&#x2122; file, HTML file, and XML file or in another relational database management system (RDBMS) format. In one embodiment, the technique is realized through a method and a system for automatically exporting results from a business intelligence, OLAP, or other reporting system. One embodiment of the present invention involves a method of enabling a user to designate a report desired for execution as an export-report. When the report is processed, the report is exported into a datamart or other format and made available for access by the user through one or more different types of user devices. The user may then further manipulate or export the results from the datamart to a file type as desired.

System and method for exporting report results from a reporting system

Growing community of inventors

San Marcos, CA, United States of America

Scott Cappiello